Mastering Git and GitHub: the hands-on workshop {https://t.co/1ltLOvfBBM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
Analyze your Twitter timeline with {rtweet} and {lubridate} {https://t.co/iyxJXc04YX} #rstats #DataScience
— R-bloggers (@Rbloggers) May 8, 2022
Comparing performances of CSV to RDS, Parquet, and Feather file formats in R {https://t.co/WxaqE5HIMt} #rstats #DataScience
— R-bloggers (@Rbloggers) May 9, 2022
The basics of prototyping and exporting your plots in R {https://t.co/1r6nIkXBl1} #rstats #DataScience
— R-bloggers (@Rbloggers) May 6, 2022
5 Key Data Visualization Principles Explained – Examples in R {https://t.co/3EnV3282sz} #rstats #DataScience
— R-bloggers (@Rbloggers) May 10, 2022
How to embed a Shiny app into your blog posts {https://t.co/HFoXeeByVq} #rstats #DataScience
— R-bloggers (@Rbloggers) May 9, 2022
Model evaluation with presence points and raster predictions {https://t.co/k54Ut1abRJ} #rstats #DataScience
— R-bloggers (@Rbloggers) May 6, 2022
Storytelling in ggplot using rounded rectangles {https://t.co/aPOAp1Mi4P} #rstats #DataScience
— R-bloggers (@Rbloggers) May 4, 2022
Supervised Learning Using Baysian Decision Rule {https://t.co/Jgfn7IXrxE} #rstats #DataScience
— R-bloggers (@Rbloggers) May 9, 2022
Three packages that port the tidyverse to Python {https://t.co/WGQVAchPBp} #rstats #DataScience
— R-bloggers (@Rbloggers) May 10, 2022
Learning Path: Introduction to R {https://t.co/H9pv6RhNk1} #rstats #DataScience
— R-bloggers (@Rbloggers) May 9, 2022
Building a multi-session {shiny} application with {brochure} {https://t.co/p4k3MUUrje} #rstats #DataScience
— R-bloggers (@Rbloggers) May 9, 2022
Bayesian analyses made easy: GLMMs in R package brms {https://t.co/POSpLZRPa1} #rstats #DataScience
— R-bloggers (@Rbloggers) April 18, 2022
R and Excel: How to Combine the Best of Both Worlds {https://t.co/29X4eIvwp3} #rstats #DataScience
— R-bloggers (@Rbloggers) April 29, 2022
Data Cleaning in R: 2 R Packages to Clean and Validate Datasets {https://t.co/MibZ2lNmMS} #rstats #DataScience
— R-bloggers (@Rbloggers) May 3, 2022
Mastering Git and GitHub: the hands-on workshop {https://t.co/1ltLOvfBBM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
How to create your own functions in R {https://t.co/2AEuycBrGe} #rstats #DataScience
— R-bloggers (@Rbloggers) April 21, 2022
Using Shiny to Create an Academic Poster {https://t.co/LUZi8zMda5} #rstats #DataScience
— R-bloggers (@Rbloggers) April 25, 2022
Text Analysis of Job Descriptions for Data Scientists, Data Engineers, Machine Learning {https://t.co/luWLNT9vV8} #rstats #DataScience
— R-bloggers (@Rbloggers) April 18, 2022
WTF is Kubernetes and Should I Care as R User? {https://t.co/Yt6tT5g15M} #rstats #DataScience
— R-bloggers (@Rbloggers) April 17, 2022
How to reshape your data in R for analysis {https://t.co/j0N0QRuVk4} #rstats #DataScience
— R-bloggers (@Rbloggers) April 28, 2022
Analyze your Twitter timeline with {rtweet} and {lubridate} {https://t.co/iyxJXc04YX} #rstats #DataScience
— R-bloggers (@Rbloggers) May 8, 2022
Propensity Score Matching {https://t.co/j62sAAqC5M} #rstats #DataScience
— R-bloggers (@Rbloggers) April 12, 2022
Getting started with Python using R and reticulate {https://t.co/fQipgV3dVk} #rstats #DataScience
— R-bloggers (@Rbloggers) April 19, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```